Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

Ebiaredoh-Mienye, Sarah A.; Esenogho, Ebenezer; Swart, Theo G.

doi:10.11591/ijece.v11i5.pp4392-4402

Cited by 25 publications

(18 citation statements)

References 30 publications

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“…Recently, the autoencoder network is widely used for dimension reduction. Autoencoders are simple learning networks that aim to transform inputs into outputs with the least possible amount of distortion [33], [34]. As shown in Figures 3 and 4, Autoencoders are artificial neural networks (ANN) with symmetric structure, where the middle layer represents an encoding of the input data [35].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Mohammed

Soliman²,

Omara³

2023

IJECE

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<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

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Section: Dimensionality Reductionmentioning

confidence: 99%

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Mohammed

Soliman²,

Omara³

2023

IJECE

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“…According to the literature, there exist four main auto-encoder architectures, including convolutional auto-encoder, variational auto-encoder, denoising auto-encoder, and sparse auto-encoder. Auto-encoders can be adopted in several applications like data denoising and dimensionality reduction [19]. Figure 1 shows the overall network architecture.…”

Section: Auto-encodermentioning

confidence: 99%

End-to-end deep auto-encoder for segmenting a moving object with limited training data

Kebir¹,

Taibi²

2022

IJECE

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<span lang="EN-US">Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.</span>

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“…applications, were not used for analysis. In addition, studies on financial data-based models such as credit card fraud detection and credit card default prediction mainly used structured data such as usage amount [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Time-Weighted Cumulative LSTM Method Using Log Data for Predicting Credit Card Customer Turnover

Eom

Kim

2023

IEEE Access

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One of the most important challenges for credit card companies is to retain customers. To achieve this, they predict whether customers will use credit cards and respond preemptively through marketing. Improper prediction leads to an increase in marketing costs; therefore, improving the performance of the prediction model is an important challenge for credit card companies. Therefore, credit card companies are working toward increasing the predictive power of the model, but the data used for learning are limited to formal data, such as card payment data; the log history stored on the application is not used. Prediction models using sequence data, such as log history, are being studied. However, no studies so far have appropriately utilized card payment data, log history, and information on the timing of the elements constituting the sequence. In this paper, we propose a method for predicting credit card turnover using application log and credit card payment data. The method embeds log data and weights each log using a term-weighting function. In addition, the time difference between the day customers visit the app and the last day of the month in which they leave is calculated. The weight value is higher when the timespan is shorter. The weighted log data were trained with a 2-layer LSTM model and its output layer was combined with the output layer trained with the card payment data for customer turnover prediction. It was verified that the model presented in this paper had a higher performance than the model using only card payment data and the model using only a time-weighted log sequence.INDEX TERMS Term-weighting, time weighted model, log analysis, predictive models.

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Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

Cited by 25 publications

References 30 publications

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

End-to-end deep auto-encoder for segmenting a moving object with limited training data

Time-Weighted Cumulative LSTM Method Using Log Data for Predicting Credit Card Customer Turnover

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